🎓Mathematical Modeling | Scientific Computing | Transitioning to AI Systems

• Mathematical Foundations of Artificial Intelligence and Machine Learning
• Scientific Machine Learning & Data-Driven Modeling
• Dynamical Systems and Continuous-Time Models
• Optimization Methods in Learning Algorithms
• Temporal and Delay-Based Modeling for Learning Systems
• Computational Methods for Intelligent Systems

• Developed a collocation-based solver for Volterra delay differential equations
• Performed convergence and error analysis across varying delay parameters
• Implemented numerical schemes in Wolfram Mathematica
• Explored potential connections to learning-based modeling and dynamical systems

🔗 🔗 Volterra Delay Differential Equations Solver

📌 Focus: Collocation-based numerical analysis of Volterra delay differential equations with convergence and error guarantees

• Collocation-based numerical method
• Convergence & error analysis
• Wolfram Mathematica implementation
• Potential relevance to optimization and learning-based dynamical modeling

🧠 Mathematical & Computational Foundations

• Numerical Analysis & Error Estimation
• Stability & Convergence Theory
• Dynamical Systems & Continuous-Time Modeling

🤖 AI & Machine Learning Methods

• Scientific Machine Learning (SciML)
• Optimization Algorithms in Machine Learning
• Data-Driven & Physics-Informed Modeling

⚙️ Technical Tools

• Wolfram Mathematica (Advanced)
• Python (NumPy, SciPy, Scientific Computing)
• Algorithm Design & Numerical Simulation

• Email: shiva.karimi1221@gmail.com
• LinkedIn: (https://www.linkedin.com/in/shiva-karimi-research)

Interested in developing mathematically grounded computational methods with potential applications in scientific machine learning and complex dynamical systems